Development of a machine learning-based model for predicting individual responses to antihypertensive treatments
Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications. We used data from a pragmatic, cluster-randomized...
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Published in | Nutrition, metabolism, and cardiovascular diseases Vol. 34; no. 7; pp. 1660 - 1669 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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Elsevier B.V
01.07.2024
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Abstract | Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications.
We used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R2 = 0.28 in the test set.
The ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently.
ClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334.
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•This study used machine learning (ML) models to predict individual post-treatment blood pressure (BP).•The ML-based tools can be beneficial for determining the specific drugs and dosage promptly to achieve a target BP level.•Our findings support the possibility of applying ML techniques to individualize medication treatment of hypertension. |
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AbstractList | Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications.
We used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R
= 0.28 in the test set.
The ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently.
ClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334. Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications. We used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R2 = 0.28 in the test set. The ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently. ClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334. [Display omitted] •This study used machine learning (ML) models to predict individual post-treatment blood pressure (BP).•The ML-based tools can be beneficial for determining the specific drugs and dosage promptly to achieve a target BP level.•Our findings support the possibility of applying ML techniques to individualize medication treatment of hypertension. Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications.BACKGROUND AND AIMSPersonalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications.We used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R2 = 0.28 in the test set.METHODS AND RESULTSWe used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R2 = 0.28 in the test set.The ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently.CONCLUSIONThe ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently.ClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334.TRIAL REGISTRATIONClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334. |
Author | Lu, Jiapeng Wang, Lili Song, Jiali Yi, Jiayi Liu, Yanchen Zheng, Xin Liu, Jiamin Zhang, Haibo |
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Cites_doi | 10.1186/s13063-022-06374-x 10.1007/s11906-018-0802-1 10.1016/S0140-6736(15)00805-3 10.1016/j.jash.2016.10.005 10.7326/M14-0697 10.1016/S0140-6736(21)01330-1 10.1001/jama.2023.3704 10.1016/0002-8703(93)90210-Z 10.1002/14651858.CD003825.pub2 10.1016/j.jacc.2018.07.008 10.1016/j.jash.2009.03.001 10.1097/HJH.0000000000001052 10.2147/IBPC.S152761 10.1001/jama.2023.3322 10.1016/j.watres.2023.120470 10.1111/jch.13874 10.1161/CIRCRESAHA.120.318729 10.1161/HYPERTENSIONAHA.123.21132 10.1097/HJH.0b013e32836157be 10.1097/MD.0000000000004071 10.1161/JAHA.122.028573 10.1161/JAHA.121.023397 10.1093/cvr/cvac116 10.1161/CIRCRESAHA.121.318106 10.1089/dis.2008.1120007 10.1001/jamacardio.2017.1421 10.1016/j.jacc.2017.11.006 10.1016/S2589-7500(22)00170-4 10.1016/j.ebiom.2022.104243 10.1161/HYPERTENSIONAHA.121.18884 10.1136/bmj.326.7404.1427 |
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Keywords | BB FDA mTIS LR Predictive model TRIPOD BP SD DBP XGBoost LASSO ACEI Blood pressure CDSS ML BMI SBP R2 ACC SHAP CCB LIGHT MAE ARB Machine learning AHA Antihypertensive medication |
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References | (bib4) Sep 11 2021; 398 Agarwal, Weir (bib32) Aug 2013; 31 Musini, Nazer, Bassett, Wright (bib16) May 29 2014 Faria, Wenzel, Lee, Coderre, Nichols, Belletti (bib37) Jul-Aug 2009; 3 Xie, Atkins, Lv (bib2) Jan 30 2016; 387 Sundström, Lind, Nowrouzi (bib9) Apr 11 2023; 329 van der Linden, Agyemang, van den Born (bib35) Jun 2020; 22 King, Derington, Herrick (bib28) Aug 2023; 80 Heran, Galm, Wright (bib20) Aug 15 2012 Hae, Kang, Kim (bib14) Dec 2023; 32 Heran, Wong, Heran, Wright (bib22) Oct 8 2008; 2008 Chen, Heran, Perez, Wright (bib17) Jan 20 2010 Yusuf, Joseph, Rangarajan (bib1) 2020; 395 Carey, Muntner, Bosworth, Whelton (bib33) Sep 11 2018; 72 Law, Wald, Morris, Jordan (bib18) Jun 28 2003; 326 Josiah Willock, Miller, Mohyi, Abuzaanona, Muminovic, Levy (bib36) Jan 29 2018; 20 Collins, Reitsma, Altman, Moons (bib25) Jan 6 2015; 162 Song, Wang, Wang (bib24) May 16 2022; 23 Louca, Tran, Toit (bib23) Oct 2022; 84 Derington, Bress, Herrick (bib5) Jun 6 2023; 12 Schutte, Srinivasapura Venkateshmurthy, Mohan, Prabhakaran (bib34) Apr 2 2021; 128 Padmanabhan, Tran, Dominiczak (bib12) Apr 2 2021; 128 Whelton, Carey, Aronow (bib29) May 15 2018; 71 Cheong, Mohd Said, Muksan (bib11) 2015; 27 Boulestreau, van den Born, Lip, Gupta (bib6) Apr 5 2022; 11 Heran, Wong, Heran, Wright (bib21) Oct 8 2008; 2008 Carey (bib8) Apr 11 2023; 329 Musini, Wright, Bassett, Jauca (bib15) Oct 7 2009 Holland, Segraves, Nnadi, Belletti, Wogen, Arcona (bib38) Apr 2008; 11 Hollenberg (bib40) Feb 1993; 125 Derington, Bress, Moran (bib26) 2023; 80 Bundy, Li, Stuchlik (bib3) Jul 1 2017; 2 Oikonomou, Spatz, Suchard, Khera (bib10) Nov 2022; 4 Pioli, Ritter, de Faria, Modolo (bib42) 2018; 11 Thomopoulos, Parati, Zanchetti (bib13) Oct 2016; 34 Jiang, Calhoun, Noble (bib41) Jun 13 2023; 119 Han, Jin, Liang, Huang, Arp (bib30) Aug 9 2023; 244 Rysz, Franczyk, Rysz-Górzyńska, Gluba-Brzózka (bib39) Jul 1 2020 Paz, de-La-Sierra, Sáez (bib31) Jul 2016; 95 Levy, Willock, Burla (bib27) Dec 2016; 10 Heran, Chen, Wang, Wright (bib19) Nov 14 2012; 11 Parati, Lackland, Campbell (bib7) Sep 2022; 79 Boulestreau (10.1016/j.numecd.2024.02.014_bib6) 2022; 11 Louca (10.1016/j.numecd.2024.02.014_bib23) 2022; 84 Yusuf (10.1016/j.numecd.2024.02.014_bib1) 2020; 395 (10.1016/j.numecd.2024.02.014_bib4) 2021; 398 Derington (10.1016/j.numecd.2024.02.014_bib26) 2023; 80 Parati (10.1016/j.numecd.2024.02.014_bib7) 2022; 79 Agarwal (10.1016/j.numecd.2024.02.014_bib32) 2013; 31 Derington (10.1016/j.numecd.2024.02.014_bib5) 2023; 12 Song (10.1016/j.numecd.2024.02.014_bib24) 2022; 23 Josiah Willock (10.1016/j.numecd.2024.02.014_bib36) 2018; 20 Rysz (10.1016/j.numecd.2024.02.014_bib39) 2020 Faria (10.1016/j.numecd.2024.02.014_bib37) 2009; 3 Musini (10.1016/j.numecd.2024.02.014_bib15) 2009 Whelton (10.1016/j.numecd.2024.02.014_bib29) 2018; 71 Chen (10.1016/j.numecd.2024.02.014_bib17) 2010 Musini (10.1016/j.numecd.2024.02.014_bib16) 2014 Padmanabhan (10.1016/j.numecd.2024.02.014_bib12) 2021; 128 Han (10.1016/j.numecd.2024.02.014_bib30) 2023; 244 Cheong (10.1016/j.numecd.2024.02.014_bib11) 2015; 27 Jiang (10.1016/j.numecd.2024.02.014_bib41) 2023; 119 Collins (10.1016/j.numecd.2024.02.014_bib25) 2015; 162 Carey (10.1016/j.numecd.2024.02.014_bib8) 2023; 329 Thomopoulos (10.1016/j.numecd.2024.02.014_bib13) 2016; 34 Sundström (10.1016/j.numecd.2024.02.014_bib9) 2023; 329 Law (10.1016/j.numecd.2024.02.014_bib18) 2003; 326 Heran (10.1016/j.numecd.2024.02.014_bib22) 2008; 2008 Levy (10.1016/j.numecd.2024.02.014_bib27) 2016; 10 Schutte (10.1016/j.numecd.2024.02.014_bib34) 2021; 128 Hollenberg (10.1016/j.numecd.2024.02.014_bib40) 1993; 125 Pioli (10.1016/j.numecd.2024.02.014_bib42) 2018; 11 Heran (10.1016/j.numecd.2024.02.014_bib20) 2012 Hae (10.1016/j.numecd.2024.02.014_bib14) 2023; 32 Oikonomou (10.1016/j.numecd.2024.02.014_bib10) 2022; 4 Carey (10.1016/j.numecd.2024.02.014_bib33) 2018; 72 Heran (10.1016/j.numecd.2024.02.014_bib19) 2012; 11 King (10.1016/j.numecd.2024.02.014_bib28) 2023; 80 Paz (10.1016/j.numecd.2024.02.014_bib31) 2016; 95 Heran (10.1016/j.numecd.2024.02.014_bib21) 2008; 2008 van der Linden (10.1016/j.numecd.2024.02.014_bib35) 2020; 22 Holland (10.1016/j.numecd.2024.02.014_bib38) 2008; 11 Bundy (10.1016/j.numecd.2024.02.014_bib3) 2017; 2 Xie (10.1016/j.numecd.2024.02.014_bib2) 2016; 387 |
References_xml | – volume: 11 year: Nov 14 2012 ident: bib19 article-title: Blood pressure lowering efficacy of potassium-sparing diuretics (that block the epithelial sodium channel) for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 71 start-page: e127 year: May 15 2018 end-page: e248 ident: bib29 article-title: 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American heart association task force on clinical practice guidelines publication-title: J Am Coll Cardiol contributor: fullname: Aronow – volume: 32 year: Dec 2023 ident: bib14 article-title: Machine Learning-Based prediction of Post-Treatment ambulatory blood pressure in patients with hypertension publication-title: Blood Pres contributor: fullname: Kim – volume: 119 start-page: 1427 year: Jun 13 2023 end-page: 1440 ident: bib41 article-title: A functional connectome signature of blood pressure in >30 000 participants from the UK biobank publication-title: Cardiovasc Res contributor: fullname: Noble – volume: 11 start-page: 71 year: Apr 2008 end-page: 77 ident: bib38 article-title: Identifying barriers to hypertension care: implications for quality improvement initiatives publication-title: Dis Manag contributor: fullname: Arcona – volume: 387 start-page: 435 year: Jan 30 2016 end-page: 443 ident: bib2 article-title: Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis publication-title: Lancet (London, England) contributor: fullname: Lv – year: May 29 2014 ident: bib16 article-title: Blood pressure-lowering efficacy of monotherapy with thiazide diuretics for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 11 start-page: 73 year: 2018 end-page: 79 ident: bib42 article-title: White coat syndrome and its variations: differences and clinical impact publication-title: Integrated Blood Pres Control contributor: fullname: Modolo – volume: 23 start-page: 412 year: May 16 2022 ident: bib24 article-title: Effectiveness of a clinical decision support system for hypertension management in primary care: study protocol for a pragmatic cluster-randomized controlled trial publication-title: Trials contributor: fullname: Wang – volume: 2008 year: Oct 8 2008 ident: bib21 article-title: Blood pressure lowering efficacy of angiotensin receptor blockers for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 12 year: Jun 6 2023 ident: bib5 article-title: Antihypertensive medication regimens used by US adults with hypertension and the potential for fixed-dose combination products: the national health and nutrition examination surveys 2015 to 2020 publication-title: J Am Heart Assoc contributor: fullname: Herrick – volume: 128 start-page: 808 year: Apr 2 2021 end-page: 826 ident: bib34 article-title: Hypertension in low- and middle-income countries publication-title: Circ Res contributor: fullname: Prabhakaran – volume: 20 start-page: 4 year: Jan 29 2018 ident: bib36 article-title: Therapeutic inertia and treatment intensification publication-title: Curr Hypertens Rep contributor: fullname: Levy – volume: 3 start-page: 267 year: Jul-Aug 2009 end-page: 276 ident: bib37 article-title: A narrative review of clinical inertia: focus on hypertension publication-title: J Am Soc Hypertens contributor: fullname: Belletti – volume: 125 start-page: 604 year: Feb 1993 end-page: 608 ident: bib40 article-title: Hypertension and the kidney: determinants of the response to antihypertensive therapy and their implications publication-title: Am Heart J contributor: fullname: Hollenberg – volume: 395 start-page: 795 year: 2020 end-page: 808 ident: bib1 article-title: Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. publication-title: Mar 7 contributor: fullname: Rangarajan – volume: 162 start-page: 55 year: Jan 6 2015 end-page: 63 ident: bib25 article-title: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement publication-title: Ann Intern Med contributor: fullname: Moons – volume: 80 start-page: 1749 year: Aug 2023 end-page: 1758 ident: bib28 article-title: Single-pill combination product availability of the antihypertensive regimens used for intensive systolic blood pressure treatment in the systolic blood pressure intervention trial publication-title: Hypertension contributor: fullname: Herrick – start-page: 21 year: Jul 1 2020 ident: bib39 article-title: Pharmacogenomics of hypertension treatment publication-title: Int J Mol Sci contributor: fullname: Gluba-Brzózka – volume: 329 start-page: 1160 year: Apr 11 2023 end-page: 1169 ident: bib9 article-title: Heterogeneity in blood pressure response to 4 antihypertensive drugs: a randomized clinical trial publication-title: JAMA contributor: fullname: Nowrouzi – volume: 326 start-page: 1427 year: Jun 28 2003 ident: bib18 article-title: Value of low dose combination treatment with blood pressure lowering drugs: analysis of 354 randomised trials publication-title: Br Med J contributor: fullname: Jordan – volume: 329 start-page: 1153 year: Apr 11 2023 end-page: 1154 ident: bib8 article-title: Is personalized antihypertensive drug selection feasible? publication-title: JAMA contributor: fullname: Carey – volume: 128 start-page: 1100 year: Apr 2 2021 end-page: 1118 ident: bib12 article-title: Artificial intelligence in hypertension: seeing through a glass darkly publication-title: Circ Res contributor: fullname: Dominiczak – volume: 22 start-page: 959 year: Jun 2020 end-page: 961 ident: bib35 article-title: Hypertension control in sub-Saharan Africa: clinical inertia is another elephant in the room publication-title: J Clin Hypertens contributor: fullname: van den Born – year: Jan 20 2010 ident: bib17 article-title: Blood pressure lowering efficacy of beta-blockers as second-line therapy for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 4 start-page: e796 year: Nov 2022 end-page: e805 ident: bib10 article-title: Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials publication-title: Lancet Digit Health contributor: fullname: Khera – volume: 80 start-page: 590 year: 2023 end-page: 597 ident: bib26 article-title: Antihypertensive medication regimens used in the systolic blood pressure intervention trial. publication-title: Mar contributor: fullname: Moran – volume: 398 start-page: 957 year: Sep 11 2021 end-page: 980 ident: bib4 article-title: Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants publication-title: Lancet (London, England) – volume: 84 year: Oct 2022 ident: bib23 article-title: Machine learning integration of multimodal data identifies key features of blood pressure regulation publication-title: EBioMedicine contributor: fullname: Toit – volume: 95 year: Jul 2016 ident: bib31 article-title: Treatment efficacy of anti-hypertensive drugs in monotherapy or combination: ATOM systematic review and meta-analysis of randomized clinical trials according to PRISMA statement publication-title: Medicine (Baltim) contributor: fullname: Sáez – volume: 27 start-page: Np485 year: 2015 end-page: Np494 ident: bib11 article-title: Time to achieve first blood pressure control after diagnosis among hypertensive patients at primary health care clinics: a preliminary study. publication-title: Mar contributor: fullname: Muksan – year: Oct 7 2009 ident: bib15 article-title: Blood pressure lowering efficacy of loop diuretics for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Jauca – volume: 79 start-page: 1949 year: Sep 2022 end-page: 1961 ident: bib7 article-title: How to improve awareness, treatment, and control of hypertension in africa, and how to reduce its consequences: a call to action from the world hypertension league publication-title: Hypertension contributor: fullname: Campbell – volume: 72 start-page: 1278 year: Sep 11 2018 end-page: 1293 ident: bib33 article-title: Prevention and control of hypertension: JACC health promotion series publication-title: J Am Coll Cardiol contributor: fullname: Whelton – volume: 11 year: Apr 5 2022 ident: bib6 article-title: Malignant hypertension: current perspectives and challenges publication-title: J Am Heart Assoc contributor: fullname: Gupta – volume: 2008 year: Oct 8 2008 ident: bib22 article-title: Blood pressure lowering efficacy of angiotensin converting enzyme (ACE) inhibitors for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 31 start-page: 1692 year: Aug 2013 end-page: 1701 ident: bib32 article-title: Blood pressure response with fixed-dose combination therapy: comparing hydrochlorothiazide with amlodipine through individual-level meta-analysis publication-title: J Hypertens contributor: fullname: Weir – volume: 244 year: Aug 9 2023 ident: bib30 article-title: Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure publication-title: Water Res contributor: fullname: Arp – volume: 34 start-page: 1921 year: Oct 2016 end-page: 1932 ident: bib13 article-title: Effects of blood-pressure-lowering treatment in hypertension: 9. Discontinuations for adverse events attributed to different classes of antihypertensive drugs: meta-analyses of randomized trials publication-title: J Hypertens contributor: fullname: Zanchetti – year: Aug 15 2012 ident: bib20 article-title: Blood pressure lowering efficacy of alpha blockers for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Wright – volume: 2 start-page: 775 year: Jul 1 2017 end-page: 781 ident: bib3 article-title: Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis publication-title: JAMA cardiology contributor: fullname: Stuchlik – volume: 10 start-page: 906 year: Dec 2016 end-page: 916 ident: bib27 article-title: Total antihypertensive therapeutic intensity score and its relationship to blood pressure reduction publication-title: J Am Soc Hypertens contributor: fullname: Burla – volume: 2008 issue: 4 year: 2008 ident: 10.1016/j.numecd.2024.02.014_bib21 article-title: Blood pressure lowering efficacy of angiotensin receptor blockers for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Heran – volume: 23 start-page: 412 issue: 1 year: 2022 ident: 10.1016/j.numecd.2024.02.014_bib24 article-title: Effectiveness of a clinical decision support system for hypertension management in primary care: study protocol for a pragmatic cluster-randomized controlled trial publication-title: Trials doi: 10.1186/s13063-022-06374-x contributor: fullname: Song – volume: 20 start-page: 4 issue: 1 year: 2018 ident: 10.1016/j.numecd.2024.02.014_bib36 article-title: Therapeutic inertia and treatment intensification publication-title: Curr Hypertens Rep doi: 10.1007/s11906-018-0802-1 contributor: fullname: Josiah Willock – volume: 387 start-page: 435 issue: 10017 year: 2016 ident: 10.1016/j.numecd.2024.02.014_bib2 article-title: Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis publication-title: Lancet (London, England) doi: 10.1016/S0140-6736(15)00805-3 contributor: fullname: Xie – volume: 10 start-page: 906 issue: 12 year: 2016 ident: 10.1016/j.numecd.2024.02.014_bib27 article-title: Total antihypertensive therapeutic intensity score and its relationship to blood pressure reduction publication-title: J Am Soc Hypertens doi: 10.1016/j.jash.2016.10.005 contributor: fullname: Levy – volume: 27 start-page: Np485 issue: 2 year: 2015 ident: 10.1016/j.numecd.2024.02.014_bib11 article-title: Time to achieve first blood pressure control after diagnosis among hypertensive patients at primary health care clinics: a preliminary study. Asia Pac J Public Health publication-title: Mar contributor: fullname: Cheong – issue: 1 year: 2010 ident: 10.1016/j.numecd.2024.02.014_bib17 article-title: Blood pressure lowering efficacy of beta-blockers as second-line therapy for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Chen – volume: 162 start-page: 55 issue: 1 year: 2015 ident: 10.1016/j.numecd.2024.02.014_bib25 article-title: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement publication-title: Ann Intern Med doi: 10.7326/M14-0697 contributor: fullname: Collins – volume: 398 start-page: 957 issue: 10304 year: 2021 ident: 10.1016/j.numecd.2024.02.014_bib4 article-title: Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants publication-title: Lancet (London, England) doi: 10.1016/S0140-6736(21)01330-1 – volume: 329 start-page: 1153 issue: 14 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib8 article-title: Is personalized antihypertensive drug selection feasible? publication-title: JAMA doi: 10.1001/jama.2023.3704 contributor: fullname: Carey – volume: 125 start-page: 604 issue: 2 Pt 2 year: 1993 ident: 10.1016/j.numecd.2024.02.014_bib40 article-title: Hypertension and the kidney: determinants of the response to antihypertensive therapy and their implications publication-title: Am Heart J doi: 10.1016/0002-8703(93)90210-Z contributor: fullname: Hollenberg – volume: 80 start-page: 590 issue: 3 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib26 article-title: Antihypertensive medication regimens used in the systolic blood pressure intervention trial. Hypertension publication-title: Mar contributor: fullname: Derington – issue: 4 year: 2009 ident: 10.1016/j.numecd.2024.02.014_bib15 article-title: Blood pressure lowering efficacy of loop diuretics for primary hypertension publication-title: Cochrane Database Syst Rev doi: 10.1002/14651858.CD003825.pub2 contributor: fullname: Musini – volume: 72 start-page: 1278 issue: 11 year: 2018 ident: 10.1016/j.numecd.2024.02.014_bib33 article-title: Prevention and control of hypertension: JACC health promotion series publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2018.07.008 contributor: fullname: Carey – volume: 3 start-page: 267 issue: 4 year: 2009 ident: 10.1016/j.numecd.2024.02.014_bib37 article-title: A narrative review of clinical inertia: focus on hypertension publication-title: J Am Soc Hypertens doi: 10.1016/j.jash.2009.03.001 contributor: fullname: Faria – volume: 34 start-page: 1921 issue: 10 year: 2016 ident: 10.1016/j.numecd.2024.02.014_bib13 article-title: Effects of blood-pressure-lowering treatment in hypertension: 9. Discontinuations for adverse events attributed to different classes of antihypertensive drugs: meta-analyses of randomized trials publication-title: J Hypertens doi: 10.1097/HJH.0000000000001052 contributor: fullname: Thomopoulos – volume: 11 year: 2012 ident: 10.1016/j.numecd.2024.02.014_bib19 article-title: Blood pressure lowering efficacy of potassium-sparing diuretics (that block the epithelial sodium channel) for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Heran – volume: 11 start-page: 73 year: 2018 ident: 10.1016/j.numecd.2024.02.014_bib42 article-title: White coat syndrome and its variations: differences and clinical impact publication-title: Integrated Blood Pres Control doi: 10.2147/IBPC.S152761 contributor: fullname: Pioli – volume: 395 start-page: 795 issue: 10226 year: 2020 ident: 10.1016/j.numecd.2024.02.014_bib1 article-title: Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet (London, England) publication-title: Mar 7 contributor: fullname: Yusuf – volume: 329 start-page: 1160 issue: 14 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib9 article-title: Heterogeneity in blood pressure response to 4 antihypertensive drugs: a randomized clinical trial publication-title: JAMA doi: 10.1001/jama.2023.3322 contributor: fullname: Sundström – volume: 2008 issue: 4 year: 2008 ident: 10.1016/j.numecd.2024.02.014_bib22 article-title: Blood pressure lowering efficacy of angiotensin converting enzyme (ACE) inhibitors for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Heran – volume: 244 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib30 article-title: Developing machine learning approaches to identify candidate persistent, mobile and toxic (PMT) and very persistent and very mobile (vPvM) substances based on molecular structure publication-title: Water Res doi: 10.1016/j.watres.2023.120470 contributor: fullname: Han – volume: 22 start-page: 959 issue: 6 year: 2020 ident: 10.1016/j.numecd.2024.02.014_bib35 article-title: Hypertension control in sub-Saharan Africa: clinical inertia is another elephant in the room publication-title: J Clin Hypertens doi: 10.1111/jch.13874 contributor: fullname: van der Linden – volume: 128 start-page: 808 issue: 7 year: 2021 ident: 10.1016/j.numecd.2024.02.014_bib34 article-title: Hypertension in low- and middle-income countries publication-title: Circ Res doi: 10.1161/CIRCRESAHA.120.318729 contributor: fullname: Schutte – volume: 80 start-page: 1749 issue: 8 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib28 article-title: Single-pill combination product availability of the antihypertensive regimens used for intensive systolic blood pressure treatment in the systolic blood pressure intervention trial publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.123.21132 contributor: fullname: King – volume: 31 start-page: 1692 issue: 8 year: 2013 ident: 10.1016/j.numecd.2024.02.014_bib32 article-title: Blood pressure response with fixed-dose combination therapy: comparing hydrochlorothiazide with amlodipine through individual-level meta-analysis publication-title: J Hypertens doi: 10.1097/HJH.0b013e32836157be contributor: fullname: Agarwal – volume: 95 issue: 30 year: 2016 ident: 10.1016/j.numecd.2024.02.014_bib31 article-title: Treatment efficacy of anti-hypertensive drugs in monotherapy or combination: ATOM systematic review and meta-analysis of randomized clinical trials according to PRISMA statement publication-title: Medicine (Baltim) doi: 10.1097/MD.0000000000004071 contributor: fullname: Paz – volume: 12 issue: 11 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib5 article-title: Antihypertensive medication regimens used by US adults with hypertension and the potential for fixed-dose combination products: the national health and nutrition examination surveys 2015 to 2020 publication-title: J Am Heart Assoc doi: 10.1161/JAHA.122.028573 contributor: fullname: Derington – volume: 11 issue: 7 year: 2022 ident: 10.1016/j.numecd.2024.02.014_bib6 article-title: Malignant hypertension: current perspectives and challenges publication-title: J Am Heart Assoc doi: 10.1161/JAHA.121.023397 contributor: fullname: Boulestreau – volume: 119 start-page: 1427 issue: 6 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib41 article-title: A functional connectome signature of blood pressure in >30 000 participants from the UK biobank publication-title: Cardiovasc Res doi: 10.1093/cvr/cvac116 contributor: fullname: Jiang – volume: 128 start-page: 1100 issue: 7 year: 2021 ident: 10.1016/j.numecd.2024.02.014_bib12 article-title: Artificial intelligence in hypertension: seeing through a glass darkly publication-title: Circ Res doi: 10.1161/CIRCRESAHA.121.318106 contributor: fullname: Padmanabhan – volume: 11 start-page: 71 issue: 2 year: 2008 ident: 10.1016/j.numecd.2024.02.014_bib38 article-title: Identifying barriers to hypertension care: implications for quality improvement initiatives publication-title: Dis Manag doi: 10.1089/dis.2008.1120007 contributor: fullname: Holland – issue: 8 year: 2012 ident: 10.1016/j.numecd.2024.02.014_bib20 article-title: Blood pressure lowering efficacy of alpha blockers for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Heran – start-page: 21 issue: 13 year: 2020 ident: 10.1016/j.numecd.2024.02.014_bib39 article-title: Pharmacogenomics of hypertension treatment publication-title: Int J Mol Sci contributor: fullname: Rysz – volume: 2 start-page: 775 issue: 7 year: 2017 ident: 10.1016/j.numecd.2024.02.014_bib3 article-title: Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis publication-title: JAMA cardiology doi: 10.1001/jamacardio.2017.1421 contributor: fullname: Bundy – volume: 32 issue: 1 year: 2023 ident: 10.1016/j.numecd.2024.02.014_bib14 article-title: Machine Learning-Based prediction of Post-Treatment ambulatory blood pressure in patients with hypertension publication-title: Blood Pres contributor: fullname: Hae – volume: 71 start-page: e127 issue: 19 year: 2018 ident: 10.1016/j.numecd.2024.02.014_bib29 publication-title: J Am Coll Cardiol doi: 10.1016/j.jacc.2017.11.006 contributor: fullname: Whelton – issue: 5 year: 2014 ident: 10.1016/j.numecd.2024.02.014_bib16 article-title: Blood pressure-lowering efficacy of monotherapy with thiazide diuretics for primary hypertension publication-title: Cochrane Database Syst Rev contributor: fullname: Musini – volume: 4 start-page: e796 issue: 11 year: 2022 ident: 10.1016/j.numecd.2024.02.014_bib10 article-title: Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials publication-title: Lancet Digit Health doi: 10.1016/S2589-7500(22)00170-4 contributor: fullname: Oikonomou – volume: 84 year: 2022 ident: 10.1016/j.numecd.2024.02.014_bib23 article-title: Machine learning integration of multimodal data identifies key features of blood pressure regulation publication-title: EBioMedicine doi: 10.1016/j.ebiom.2022.104243 contributor: fullname: Louca – volume: 79 start-page: 1949 issue: 9 year: 2022 ident: 10.1016/j.numecd.2024.02.014_bib7 article-title: How to improve awareness, treatment, and control of hypertension in africa, and how to reduce its consequences: a call to action from the world hypertension league publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.121.18884 contributor: fullname: Parati – volume: 326 start-page: 1427 issue: 7404 year: 2003 ident: 10.1016/j.numecd.2024.02.014_bib18 article-title: Value of low dose combination treatment with blood pressure lowering drugs: analysis of 354 randomised trials publication-title: Br Med J doi: 10.1136/bmj.326.7404.1427 contributor: fullname: Law |
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SubjectTerms | Aged Antihypertensive Agents - adverse effects Antihypertensive Agents - therapeutic use Antihypertensive medication Blood pressure Blood Pressure - drug effects China - epidemiology Clinical Decision-Making Decision Support Techniques Female Humans Hypertension - diagnosis Hypertension - drug therapy Hypertension - physiopathology Machine Learning Male Middle Aged Precision Medicine Predictive model Predictive Value of Tests Randomized Controlled Trials as Topic Risk Factors Treatment Outcome |
Title | Development of a machine learning-based model for predicting individual responses to antihypertensive treatments |
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